SVIn2: An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor

Abstract

This paper presents a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system with loop-closing and relocalization capabilities targeted for the underwater domain.Our previous work, SVIn, augmented the state-of-the-art visual-inertial state estimation package OKVIS to accommodate acoustic data from sonar in a non-linear optimization-based framework. This paper addresses drift and loss of localization -one of the main problems affecting other packages in underwater domain -by providing the following main contributions: a robust initialization method to refine scale using depth measurements, a fast preprocessing step to enhance the image quality, and a real-time loop-closing and relocalization method using bag of words (BoW). An additional contribution is the addition of depth measurements from a pressure sensor to the tightly-coupled optimization formulation. Experimental results on datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle from challenging underwater environments with poor visibility demonstrate performance never achieved before in terms of accuracy and robustness.

An Integrated Visual Odometry System for Underwater Vehicles

“…The ORB-SLAM2 method has been implemented for navigating AUVs in [53], and for mapping an underwater cave in [54]. The VINS-Mono and OKVIS algorithms, integrating inertial sensors, have been used to track the pose of a camera in an underwater environment [33] [34].…”

“…Because of that, camera pose estimation and mapping of underwater structures are processed simultaneously by minimizing the cost function. Sharmin improved the method by applying a robust initialization method, an image enhanced technique, and a loop-closure technique in[34]. However, these methods were not tested by quantitative evaluation methods in the underwater environment.…”

Elevation Angle Estimations of Wide-Beam Acoustic Sonar Measurements for Autonomous Underwater Karst Exploration

“…In particular, a large focus has been put in the robotic field on terrestrial and aerial applications based on camera sensors [7,8]. Some efforts were also made to use optical sensors for underwater applications [9][10][11], but were rather limited as they were highly dependent on water conditions (turbidity, luminosity). Hence we cannot guarantee the robot localization in an unknown environment based solely on vision sensors.…”

“…They used an array of 54 pencil-beam sonars placed around their vehicle in order to map the environment and recognize previously mapped region for SLAM purposes. Rahman et al [10] proposed a system for underwater cave exploration. They notably leverage profiling sonar, and inertial and depth sensors to strengthen their approach based on stereo camera vision.…”

Towards the Design and Implementation of an Image-Based Navigation System of an Autonomous Underwater Vehicle Combining a Color Recognition Technique and a Fuzzy Logic Controller

“…Machine vision processing technology mainly employs cameras and computers to analyze images and provide control information of the AUV drive system. There have been numerous research studies on the application of an AUV equipped with machine vision processing technology, such as the detection of underwater man-made structures and pipeline detection [3][4][5][6][7][8][9][10], auxiliary sonar image navigation [11,12], simultaneous localization and mapping (SLAM) [13][14][15][16][17], obstacle avoidance [18,19], identifying and tracking the habitats of sea animals [20], underwater docking systems [21][22][23][24][25][26][27], and object tracking [28][29][30][31][32][33][34][35][36].…”